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CGOTSU_THRESHOLD

CGOTSU_THRESHOLD
  The purpose of this function is to find an optimal threshold for separating
  a bimodal distribution of pixels in an image histogram. The algorithm used is the
  "faster approach" algorithm explained
  `on this web page <http://www.labbookpages.co.uk/software/imgProc/otsuThreshold.html>`.
 

Categories


    Utility
   

Returns


    The optimal threshold that separates two populations of pixels is returned.
   

Params


    data: in, required,
      The data from which the histogram is created.
     

Keywords


    binsize: in, optional
      The binsize of the histogram. By default, Scott's Choice of bin size for histograms is used::
                       
          binsize = (3.5 * StdDev(data)) / N_Elements(data)^(0.3333)
         
      unless the data is byte type. Then a BINSIZE of 1 is used by default
                           
      If BINSIZE in not defined, and NBINS is defined, the BINSIZE is calcuated as::
                       
            binsize = (Max(dataToHistogram) - Min(dataToHistogram)) / (NBINS -1)
                           
      While it is pointed out in the HISTOGRAM documentation, it is extremely
      important that the BINSIZE be of the same data type as the data you are going to
      calculate the histogram of. If it is not VERY strange things can happen. I've
      tried to protect you from most of the bad things, but I don't have a high confidence
      level that I have done it for every situation. If you see something that "just don't
      look right", I would check first to see if your data types match. That might solve
      all your problems.
    example: in, optional, type=boolean, default=0
      Set this keyword if you wish to use the example data from the
      `reference documentation <http://www.labbookpages.co.uk/software/imgProc/otsuThreshold.html>`.
    histdata: out, optional
      The output value of the internal HISTOGRAM command.
    l64: in, optional, type=boolean, default=0
      If set, the return value of HISTOGRAM are 64-bit integers, rather than
      the default 32-bit integers. Set by default for data types greater than or
      equal to 12.
    locations: out, optional
      Starting locations of each bin. (See the HISTOGRAM documentation for details.)
    max: in, optional
      The maximum value to use in calculating input histogram. Equivalent to the MAX keyword
      in the HISTOGRAM documentation.
    min: in, optional
      The minimum value to use in calculating input histogram. Equivalent to the MIN keyword
      in the HISTOGRAM documentation.
    missing: in, optional
      The value that should be represented as "missing" and not used in the histogram.
      Be aware that if the input data is not of type "float" or "double" that the input
      data will be converted to floating point prior to calculating the histogram.
    nan: in, optional, type=boolean, default=0
      If set, ignore NAN values in calculating and plotting histogram.
    nbins: in, optional, type=integer
      The number of output bins in the histogram. Meaning is slightly different from
      meaning in the HISTOGRAM command. Used only to calculate BINSIZE when BINSIZE is
      not specified. In this case, binsize = rangeofData/(nbins-1).
    omax: out, optional
      The maximum output value used to construct the histogram. (See HISTOGRAM documentation.)
    omin: out, optional
      The minimum output value used to construct the histogram. (See HISTOGRAM documentation.)
    plotit: in, optional, type=boolean, default=0
      If this keyword is set, a histogram of the data is plotted along with a plot of the
      between class variance to show the selected threshold.
    reverse_indices: out, optional
      The list of reverse indices returned from the HISTOGRAM command. (See HISTOGRAM documentation.)
         

Examples


    Set the `Example` keyword to use the data from the reference page.
       

Author


    FANNING SOFTWARE CONSULTING::
      David W. Fanning
      1645 Sheely Drive
      Fort Collins, CO 80526 USA
      Phone: 970-221-0438
      E-mail: david@idlcoyote.com
      Coyote's Guide to IDL Programming: http://www.idlcoyote.com

History


    Change History::
      Written by: David W. Fanning, 13 November 2012, from a program named OTSU_THRESHOLD by Carl Salvaggio and
          modified by Gianguido Cianci.
      The OTSU_THRESHOLD algorithm used previously made many assumptions about the data. The algorithm used here
          has been completely rewritten to comply with the values in the reference page and to avoid making
          assumptions about the data used to create the histogram. 21 November 2012. DWF.
      Modified to set L64 keyword if data type GE 14 (suggested by user). 22 November 2012. DWF.
      Modified the threshold value to use DIndGen instead of IndGen, which was causing incorrect
          results with integer data. 24 November 2012. DWF.
       

Copyright


    Copyright (c) 2012, Fanning Software Consulting, Inc.



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